Where Should We Go? Internet Searches and Tourist Arrivals

IMF Working Papers, January 31, 2020

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Author: Serhan Cevik
Date: January 31, 2020
Scope: Investigates whether travel-related online search queries enhance accuracy in forecasting tourist arrivals to The Bahamas from the U.S.
Key conclusion: The Google Trends-augmented model improves predictability of tourist arrivals by about 30 percent compared to the benchmark ARIMA model and more than 20 percent compared to the model extended only with income and relative prices.

Internet search data are a new source of high-frequency information that can potentially improve the precision of macroeconomic forecasting, especially in areas with data constraints.
The forecast model incorporating internet search data provides additional information about tourist flows over:
- a univariate approach using the traditional autoregressive integrated moving average (ARIMA) model, and
- multivariate models with macroeconomic indicators.
Quantified improvements in predictability:
- about 30 percent improvement versus the benchmark ARIMA model;
- more than 20 percent improvement versus the model extended only with income and relative prices.

Comparative forecasting approach using:
- ARIMA (autoregressive integrated moving average) as the benchmark univariate model;
- multivariate models with macroeconomic indicators including income and relative prices;
- a Google Trends-augmented model incorporating travel-related online search queries.
Target variable: tourist arrivals to The Bahamas from the U.S.

High-frequency internet search data (Google Trends) can augment traditional forecasting frameworks to better predict tourist flows.
Incorporation of search-volume indicators can be especially valuable for countries and sectors with limited timely data.
Tourism-sector analysts and policymakers could consider integrating online search indicators into surveillance and planning tools to enhance situational awareness and forecasting accuracy.